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JRM Vol.31 No.1 pp. 70-77
doi: 10.20965/jrm.2019.p0070
(2019)

Paper:

Research on Snoring Recognition Algorithms

Yongping Dan*, Yaming Song*, Dongyun Wang**, Fenghui Zhang*, Wei Liu*, and Xiaohui Lu*

*School of Electric and Information Engineer, Zhongyuan University of Technology
No.41 Zhangyuan Road, Zhengzhou, Henan 450007, China

**College of Information Engineering, Huanghuai University
6 Kai Yuan Road, Zhumadian, Henan 463000, China

Received:
May 21, 2018
Accepted:
September 28, 2018
Published:
February 20, 2019
Keywords:
snoring recognition, endpoint detection, MFCC feature extraction, support vector machine
Abstract
Research on Snoring Recognition Algorithms

Snoring signal endpoint detection

A snoring recognition algorithm based on machine learning is proposed to effectively and precisely recognize snoring. To obtain a dataset, the speech endpoint detection algorithm and Mel frequency cepstrum coefficient feature extraction algorithm are applied to process speech signal samples. The dataset is classified into snoring and nonsnoring data (other speech signals) using support vector machines. Experimental results show that the algorithm recognizes snoring signals with a high accuracy rate of 97% and positively impacts subsequent research and related engineering applications.

Cite this article as:
Y. Dan, Y. Song, D. Wang, F. Zhang, W. Liu, and X. Lu, “Research on Snoring Recognition Algorithms,” J. Robot. Mechatron., Vol.31, No.1, pp. 70-77, 2019.
Data files:
References
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Last updated on Nov. 18, 2019